Overview

Dataset statistics

Number of variables35
Number of observations72983
Missing cells149271
Missing cells (%)5.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory19.5 MiB
Average record size in memory280.0 B

Variable types

Categorical18
Numeric16
Boolean1

Alerts

PurchDate has a high cardinality: 517 distinct values High cardinality
Model has a high cardinality: 1063 distinct values High cardinality
Trim has a high cardinality: 134 distinct values High cardinality
SubModel has a high cardinality: 863 distinct values High cardinality
VehYear is highly correlated with VehicleAge and 8 other fieldsHigh correlation
VehicleAge is highly correlated with VehYear and 5 other fieldsHigh correlation
MMRAcquisitionAuctionAveragePrice is highly correlated with VehYear and 9 other fieldsHigh correlation
MMRAcquisitionAuctionCleanPrice is highly correlated with VehYear and 9 other fieldsHigh correlation
MMRAcquisitionRetailAveragePrice is highly correlated with VehYear and 8 other fieldsHigh correlation
MMRAcquisitonRetailCleanPrice is highly correlated with VehYear and 8 other fieldsHigh correlation
MMRCurrentAuctionAveragePrice is highly correlated with VehYear and 9 other fieldsHigh correlation
MMRCurrentAuctionCleanPrice is highly correlated with VehYear and 9 other fieldsHigh correlation
MMRCurrentRetailAveragePrice is highly correlated with VehYear and 9 other fieldsHigh correlation
MMRCurrentRetailCleanPrice is highly correlated with VehYear and 8 other fieldsHigh correlation
VehBCost is highly correlated with MMRAcquisitionAuctionAveragePrice and 7 other fieldsHigh correlation
VehYear is highly correlated with VehicleAge and 8 other fieldsHigh correlation
VehicleAge is highly correlated with VehYear and 5 other fieldsHigh correlation
MMRAcquisitionAuctionAveragePrice is highly correlated with VehYear and 9 other fieldsHigh correlation
MMRAcquisitionAuctionCleanPrice is highly correlated with VehYear and 9 other fieldsHigh correlation
MMRAcquisitionRetailAveragePrice is highly correlated with VehYear and 8 other fieldsHigh correlation
MMRAcquisitonRetailCleanPrice is highly correlated with VehYear and 8 other fieldsHigh correlation
MMRCurrentAuctionAveragePrice is highly correlated with VehYear and 9 other fieldsHigh correlation
MMRCurrentAuctionCleanPrice is highly correlated with VehYear and 9 other fieldsHigh correlation
MMRCurrentRetailAveragePrice is highly correlated with VehYear and 9 other fieldsHigh correlation
MMRCurrentRetailCleanPrice is highly correlated with VehYear and 8 other fieldsHigh correlation
VehBCost is highly correlated with MMRAcquisitionAuctionAveragePrice and 7 other fieldsHigh correlation
VehYear is highly correlated with VehicleAgeHigh correlation
VehicleAge is highly correlated with VehYearHigh correlation
MMRAcquisitionAuctionAveragePrice is highly correlated with MMRAcquisitionAuctionCleanPrice and 7 other fieldsHigh correlation
MMRAcquisitionAuctionCleanPrice is highly correlated with MMRAcquisitionAuctionAveragePrice and 7 other fieldsHigh correlation
MMRAcquisitionRetailAveragePrice is highly correlated with MMRAcquisitionAuctionAveragePrice and 7 other fieldsHigh correlation
MMRAcquisitonRetailCleanPrice is highly correlated with MMRAcquisitionAuctionAveragePrice and 7 other fieldsHigh correlation
MMRCurrentAuctionAveragePrice is highly correlated with MMRAcquisitionAuctionAveragePrice and 7 other fieldsHigh correlation
MMRCurrentAuctionCleanPrice is highly correlated with MMRAcquisitionAuctionAveragePrice and 7 other fieldsHigh correlation
MMRCurrentRetailAveragePrice is highly correlated with MMRAcquisitionAuctionAveragePrice and 7 other fieldsHigh correlation
MMRCurrentRetailCleanPrice is highly correlated with MMRAcquisitionAuctionAveragePrice and 7 other fieldsHigh correlation
VehBCost is highly correlated with MMRAcquisitionAuctionAveragePrice and 7 other fieldsHigh correlation
TopThreeAmericanName is highly correlated with Nationality and 1 other fieldsHigh correlation
WheelTypeID is highly correlated with WheelTypeHigh correlation
Nationality is highly correlated with TopThreeAmericanName and 1 other fieldsHigh correlation
VNST is highly correlated with AuctionHigh correlation
Auction is highly correlated with VNSTHigh correlation
Make is highly correlated with TopThreeAmericanName and 1 other fieldsHigh correlation
WheelType is highly correlated with WheelTypeIDHigh correlation
Auction is highly correlated with VNSTHigh correlation
VehYear is highly correlated with VehicleAgeHigh correlation
VehicleAge is highly correlated with VehYear and 6 other fieldsHigh correlation
Make is highly correlated with Nationality and 3 other fieldsHigh correlation
WheelTypeID is highly correlated with WheelTypeHigh correlation
WheelType is highly correlated with WheelTypeID and 1 other fieldsHigh correlation
Nationality is highly correlated with Make and 1 other fieldsHigh correlation
Size is highly correlated with Make and 3 other fieldsHigh correlation
TopThreeAmericanName is highly correlated with Make and 2 other fieldsHigh correlation
MMRAcquisitionAuctionAveragePrice is highly correlated with VehicleAge and 8 other fieldsHigh correlation
MMRAcquisitionAuctionCleanPrice is highly correlated with VehicleAge and 8 other fieldsHigh correlation
MMRAcquisitionRetailAveragePrice is highly correlated with MMRAcquisitionAuctionAveragePrice and 7 other fieldsHigh correlation
MMRAcquisitonRetailCleanPrice is highly correlated with MMRAcquisitionAuctionAveragePrice and 7 other fieldsHigh correlation
MMRCurrentAuctionAveragePrice is highly correlated with VehicleAge and 9 other fieldsHigh correlation
MMRCurrentAuctionCleanPrice is highly correlated with VehicleAge and 8 other fieldsHigh correlation
MMRCurrentRetailAveragePrice is highly correlated with VehicleAge and 8 other fieldsHigh correlation
MMRCurrentRetailCleanPrice is highly correlated with MMRAcquisitionAuctionAveragePrice and 7 other fieldsHigh correlation
PRIMEUNIT is highly correlated with MMRCurrentAuctionAveragePrice and 1 other fieldsHigh correlation
BYRNO is highly correlated with VehicleAge and 2 other fieldsHigh correlation
VNZIP1 is highly correlated with BYRNO and 1 other fieldsHigh correlation
VNST is highly correlated with Auction and 2 other fieldsHigh correlation
VehBCost is highly correlated with MMRAcquisitionAuctionAveragePrice and 8 other fieldsHigh correlation
WarrantyCost is highly correlated with Make and 1 other fieldsHigh correlation
pMonth is highly correlated with seasonHigh correlation
season is highly correlated with pMonthHigh correlation
Trim has 2360 (3.2%) missing values Missing
WheelTypeID has 3169 (4.3%) missing values Missing
WheelType has 3174 (4.3%) missing values Missing
PRIMEUNIT has 69564 (95.3%) missing values Missing
AUCGUART has 69564 (95.3%) missing values Missing
MMRAcquisitionAuctionAveragePrice has 828 (1.1%) zeros Zeros
MMRAcquisitionRetailAveragePrice has 828 (1.1%) zeros Zeros
MMRAcquisitonRetailCleanPrice has 828 (1.1%) zeros Zeros

Reproduction

Analysis started2022-06-09 15:16:06.598608
Analysis finished2022-06-09 15:16:53.124054
Duration46.53 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

IsBadBuy
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size570.3 KiB
0
64007 
1
8976 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
064007
87.7%
18976
 
12.3%

Length

2022-06-09T17:16:53.162811image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-09T17:16:53.226948image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
064007
87.7%
18976
 
12.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

PurchDate
Categorical

HIGH CARDINALITY

Distinct517
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size570.3 KiB
2010-11-23
 
384
2009-02-25
 
379
2010-12-08
 
372
2010-10-13
 
359
2009-08-26
 
359
Other values (512)
71130 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row2009-12-07
2nd row2009-12-07
3rd row2009-12-07
4th row2009-12-07
5th row2009-12-07

Common Values

ValueCountFrequency (%)
2010-11-23384
 
0.5%
2009-02-25379
 
0.5%
2010-12-08372
 
0.5%
2010-10-13359
 
0.5%
2009-08-26359
 
0.5%
2010-11-03357
 
0.5%
2009-02-18357
 
0.5%
2010-11-17343
 
0.5%
2010-01-21335
 
0.5%
2010-10-20329
 
0.5%
Other values (507)69409
95.1%

Length

2022-06-09T17:16:53.280465image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2010-11-23384
 
0.5%
2009-02-25379
 
0.5%
2010-12-08372
 
0.5%
2010-10-13359
 
0.5%
2009-08-26359
 
0.5%
2010-11-03357
 
0.5%
2009-02-18357
 
0.5%
2010-11-17343
 
0.5%
2010-01-21335
 
0.5%
2010-10-20329
 
0.5%
Other values (507)69409
95.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Auction
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size570.3 KiB
MANHEIM
41043 
OTHER
17501 
ADESA
14439 

Length

Max length7
Median length7
Mean length6.124727676
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowADESA
2nd rowADESA
3rd rowADESA
4th rowADESA
5th rowADESA

Common Values

ValueCountFrequency (%)
MANHEIM41043
56.2%
OTHER17501
24.0%
ADESA14439
 
19.8%

Length

2022-06-09T17:16:53.365286image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-09T17:16:53.415710image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
manheim41043
56.2%
other17501
24.0%
adesa14439
 
19.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

VehYear
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2005.343052
Minimum2001
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size570.3 KiB
2022-06-09T17:16:53.486384image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2001
5-th percentile2002
Q12004
median2005
Q32007
95-th percentile2008
Maximum2010
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.731251606
Coefficient of variation (CV)0.0008633194224
Kurtosis-0.3258624512
Mean2005.343052
Median Absolute Deviation (MAD)1
Skewness-0.337361228
Sum146355952
Variance2.997232123
MonotonicityNot monotonic
2022-06-09T17:16:53.552649image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
200617043
23.4%
200515489
21.2%
200711423
15.7%
200410207
14.0%
20086885
9.4%
20036227
 
8.5%
20023405
 
4.7%
20011481
 
2.0%
2009822
 
1.1%
20101
 
< 0.1%
ValueCountFrequency (%)
20011481
 
2.0%
20023405
 
4.7%
20036227
 
8.5%
200410207
14.0%
200515489
21.2%
200617043
23.4%
200711423
15.7%
20086885
9.4%
2009822
 
1.1%
20101
 
< 0.1%
ValueCountFrequency (%)
20101
 
< 0.1%
2009822
 
1.1%
20086885
9.4%
200711423
15.7%
200617043
23.4%
200515489
21.2%
200410207
14.0%
20036227
 
8.5%
20023405
 
4.7%
20011481
 
2.0%

VehicleAge
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.176643876
Minimum0
Maximum9
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size570.3 KiB
2022-06-09T17:16:53.633835image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median4
Q35
95-th percentile7
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.712210288
Coefficient of variation (CV)0.4099488342
Kurtosis-0.2092717782
Mean4.176643876
Median Absolute Deviation (MAD)1
Skewness0.3936160141
Sum304824
Variance2.93166407
MonotonicityNot monotonic
2022-06-09T17:16:53.816335image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
417013
23.3%
315902
21.8%
512956
17.8%
28482
11.6%
68022
11.0%
74646
 
6.4%
13094
 
4.2%
82220
 
3.0%
9646
 
0.9%
02
 
< 0.1%
ValueCountFrequency (%)
02
 
< 0.1%
13094
 
4.2%
28482
11.6%
315902
21.8%
417013
23.3%
512956
17.8%
68022
11.0%
74646
 
6.4%
82220
 
3.0%
9646
 
0.9%
ValueCountFrequency (%)
9646
 
0.9%
82220
 
3.0%
74646
 
6.4%
68022
11.0%
512956
17.8%
417013
23.3%
315902
21.8%
28482
11.6%
13094
 
4.2%
02
 
< 0.1%

Make
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size570.3 KiB
CHEVROLET
17248 
DODGE
12912 
FORD
11305 
CHRYSLER
8844 
PONTIAC
4258 
Other values (28)
18416 

Length

Max length12
Median length6
Mean length6.431360728
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowMAZDA
2nd rowDODGE
3rd rowDODGE
4th rowDODGE
5th rowFORD

Common Values

ValueCountFrequency (%)
CHEVROLET17248
23.6%
DODGE12912
17.7%
FORD11305
15.5%
CHRYSLER8844
12.1%
PONTIAC4258
 
5.8%
KIA2484
 
3.4%
SATURN2163
 
3.0%
NISSAN2085
 
2.9%
HYUNDAI1811
 
2.5%
JEEP1644
 
2.3%
Other values (23)8229
11.3%

Length

2022-06-09T17:16:53.899424image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
chevrolet17248
23.6%
dodge12912
17.7%
ford11305
15.5%
chrysler8844
12.1%
pontiac4258
 
5.8%
kia2484
 
3.4%
saturn2163
 
3.0%
nissan2085
 
2.9%
hyundai1811
 
2.5%
jeep1644
 
2.3%
Other values (22)8230
11.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Model
Categorical

HIGH CARDINALITY

Distinct1063
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size570.3 KiB
PT CRUISER
 
2329
IMPALA
 
1990
TAURUS
 
1425
CALIBER
 
1375
CARAVAN GRAND FWD V6
 
1289
Other values (1058)
64575 

Length

Max length20
Median length15
Mean length13.72755299
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique161 ?
Unique (%)0.2%

Sample

1st rowMAZDA3
2nd row1500 RAM PICKUP 2WD
3rd rowSTRATUS V6
4th rowNEON
5th rowFOCUS

Common Values

ValueCountFrequency (%)
PT CRUISER2329
 
3.2%
IMPALA1990
 
2.7%
TAURUS1425
 
2.0%
CALIBER1375
 
1.9%
CARAVAN GRAND FWD V61289
 
1.8%
MALIBU 4C1225
 
1.7%
TAURUS 3.0L V6 EFI1160
 
1.6%
SEBRING 4C1157
 
1.6%
COBALT1106
 
1.5%
PT CRUISER 2.4L I4 S1104
 
1.5%
Other values (1053)58823
80.6%

Length

2022-06-09T17:16:53.999551image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
v628571
 
13.1%
4c11229
 
5.2%
2wd11009
 
5.1%
i49070
 
4.2%
fwd7197
 
3.3%
impala4784
 
2.2%
grand4222
 
1.9%
2.4l3777
 
1.7%
cruiser3729
 
1.7%
pt3728
 
1.7%
Other values (378)130515
59.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Trim
Categorical

HIGH CARDINALITY
MISSING

Distinct134
Distinct (%)0.2%
Missing2360
Missing (%)3.2%
Memory size570.3 KiB
Bas
13950 
LS
10174 
SE
9348 
SXT
3825 
LT
3540 
Other values (129)
29786 

Length

Max length3
Median length3
Mean length2.46630701
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)< 0.1%

Sample

1st rowi
2nd rowST
3rd rowSXT
4th rowSXT
5th rowZX3

Common Values

ValueCountFrequency (%)
Bas13950
19.1%
LS10174
13.9%
SE9348
12.8%
SXT3825
 
5.2%
LT3540
 
4.9%
LX2417
 
3.3%
Tou2256
 
3.1%
EX2120
 
2.9%
SEL1360
 
1.9%
XLT1357
 
1.9%
Other values (124)20276
27.8%
(Missing)2360
 
3.2%

Length

2022-06-09T17:16:54.099658image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bas13950
19.8%
ls10174
14.4%
se9355
13.2%
sxt3825
 
5.4%
lt3540
 
5.0%
lx2417
 
3.4%
tou2256
 
3.2%
ex2128
 
3.0%
sel1360
 
1.9%
xlt1357
 
1.9%
Other values (120)20269
28.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

SubModel
Categorical

HIGH CARDINALITY

Distinct863
Distinct (%)1.2%
Missing8
Missing (%)< 0.1%
Memory size570.3 KiB
4D SEDAN
15236 
4D SEDAN LS
4718 
4D SEDAN SE
 
3859
4D WAGON
 
2230
MINIVAN 3.3L
 
1258
Other values (858)
45674 

Length

Max length32
Median length11
Mean length12.20298732
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique161 ?
Unique (%)0.2%

Sample

1st row4D SEDAN I
2nd rowQUAD CAB 4.7L SLT
3rd row4D SEDAN SXT FFV
4th row4D SEDAN
5th row2D COUPE ZX3

Common Values

ValueCountFrequency (%)
4D SEDAN15236
 
20.9%
4D SEDAN LS4718
 
6.5%
4D SEDAN SE3859
 
5.3%
4D WAGON2230
 
3.1%
MINIVAN 3.3L1258
 
1.7%
4D SUV 4.2L LS1193
 
1.6%
4D SEDAN LT1129
 
1.5%
4D SEDAN SXT FFV1094
 
1.5%
2D COUPE1072
 
1.5%
4D SEDAN LX1068
 
1.5%
Other values (853)40118
55.0%

Length

2022-06-09T17:16:54.199740image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4d59830
27.6%
sedan42228
19.5%
ls9143
 
4.2%
suv8391
 
3.9%
se5949
 
2.7%
wagon4162
 
1.9%
cab3908
 
1.8%
sport3538
 
1.6%
ffv3394
 
1.6%
2d3354
 
1.5%
Other values (263)72555
33.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Color
Categorical

Distinct16
Distinct (%)< 0.1%
Missing8
Missing (%)< 0.1%
Memory size570.3 KiB
SILVER
14875 
WHITE
12123 
BLUE
10347 
GREY
7887 
BLACK
7627 
Other values (11)
20116 

Length

Max length9
Median length5
Mean length4.758136348
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRED
2nd rowWHITE
3rd rowMAROON
4th rowSILVER
5th rowSILVER

Common Values

ValueCountFrequency (%)
SILVER14875
20.4%
WHITE12123
16.6%
BLUE10347
14.2%
GREY7887
10.8%
BLACK7627
10.5%
RED6257
8.6%
GOLD5231
 
7.2%
GREEN3194
 
4.4%
MAROON2046
 
2.8%
BEIGE1584
 
2.2%
Other values (6)1804
 
2.5%

Length

2022-06-09T17:16:54.303790image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
silver14875
20.4%
white12123
16.6%
blue10347
14.2%
grey7887
10.8%
black7627
10.4%
red6257
8.6%
gold5231
 
7.2%
green3194
 
4.4%
maroon2046
 
2.8%
beige1584
 
2.2%
Other values (7)1898
 
2.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Transmission
Categorical

Distinct3
Distinct (%)< 0.1%
Missing9
Missing (%)< 0.1%
Memory size570.3 KiB
AUTO
70398 
MANUAL
 
2575
Manual
 
1

Length

Max length6
Median length4
Mean length4.070600488
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowAUTO
2nd rowAUTO
3rd rowAUTO
4th rowAUTO
5th rowMANUAL

Common Values

ValueCountFrequency (%)
AUTO70398
96.5%
MANUAL2575
 
3.5%
Manual1
 
< 0.1%
(Missing)9
 
< 0.1%

Length

2022-06-09T17:16:54.388321image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-09T17:16:54.437019image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
auto70398
96.5%
manual2576
 
3.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

WheelTypeID
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing3169
Missing (%)4.3%
Memory size570.3 KiB
1.0
36050 
2.0
33004 
3.0
 
755
0.0
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row2.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.036050
49.4%
2.033004
45.2%
3.0755
 
1.0%
0.05
 
< 0.1%
(Missing)3169
 
4.3%

Length

2022-06-09T17:16:54.509045image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-09T17:16:54.560475image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.036050
51.6%
2.033004
47.3%
3.0755
 
1.1%
0.05
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

WheelType
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct3
Distinct (%)< 0.1%
Missing3174
Missing (%)4.3%
Memory size570.3 KiB
Alloy
36050 
Covers
33004 
Special
 
755

Length

Max length7
Median length5
Mean length5.494406165
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAlloy
2nd rowAlloy
3rd rowCovers
4th rowAlloy
5th rowCovers

Common Values

ValueCountFrequency (%)
Alloy36050
49.4%
Covers33004
45.2%
Special755
 
1.0%
(Missing)3174
 
4.3%

Length

2022-06-09T17:16:54.627109image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-09T17:16:54.702399image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
alloy36050
51.6%
covers33004
47.3%
special755
 
1.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

VehOdo
Real number (ℝ≥0)

Distinct39947
Distinct (%)54.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71499.99592
Minimum4825
Maximum115717
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size570.3 KiB
2022-06-09T17:16:54.775827image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum4825
5-th percentile45477
Q161837
median73361
Q382436
95-th percentile92521.8
Maximum115717
Range110892
Interquartile range (IQR)20599

Descriptive statistics

Standard deviation14578.91313
Coefficient of variation (CV)0.2039008946
Kurtosis-0.1987398752
Mean71499.99592
Median Absolute Deviation (MAD)10099
Skewness-0.4531454298
Sum5218284202
Variance212544708
MonotonicityNot monotonic
2022-06-09T17:16:54.888462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7500910
 
< 0.1%
7799510
 
< 0.1%
753719
 
< 0.1%
712259
 
< 0.1%
790159
 
< 0.1%
674649
 
< 0.1%
858848
 
< 0.1%
757868
 
< 0.1%
889588
 
< 0.1%
718238
 
< 0.1%
Other values (39937)72895
99.9%
ValueCountFrequency (%)
48251
< 0.1%
53681
< 0.1%
87061
< 0.1%
94461
< 0.1%
98781
< 0.1%
100951
< 0.1%
106431
< 0.1%
116621
< 0.1%
126281
< 0.1%
129261
< 0.1%
ValueCountFrequency (%)
1157171
< 0.1%
1150261
< 0.1%
1141841
< 0.1%
1136171
< 0.1%
1120561
< 0.1%
1120291
< 0.1%
1103181
< 0.1%
1101191
< 0.1%
1100711
< 0.1%
1100551
< 0.1%

Nationality
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing5
Missing (%)< 0.1%
Memory size570.3 KiB
AMERICAN
61028 
OTHER ASIAN
8033 
TOP LINE ASIAN
 
3722
OTHER
 
195

Length

Max length14
Median length8
Mean length8.628216723
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOTHER ASIAN
2nd rowAMERICAN
3rd rowAMERICAN
4th rowAMERICAN
5th rowAMERICAN

Common Values

ValueCountFrequency (%)
AMERICAN61028
83.6%
OTHER ASIAN8033
 
11.0%
TOP LINE ASIAN3722
 
5.1%
OTHER195
 
0.3%
(Missing)5
 
< 0.1%

Length

2022-06-09T17:16:54.981375image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-09T17:16:55.033901image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
american61028
69.0%
asian11755
 
13.3%
other8228
 
9.3%
top3722
 
4.2%
line3722
 
4.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Size
Categorical

HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing5
Missing (%)< 0.1%
Memory size570.3 KiB
MEDIUM
30785 
LARGE
8850 
MEDIUM SUV
8090 
COMPACT
7205 
VAN
5854 
Other values (7)
12194 

Length

Max length11
Median length6
Mean length6.760119488
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMEDIUM
2nd rowLARGE TRUCK
3rd rowMEDIUM
4th rowCOMPACT
5th rowCOMPACT

Common Values

ValueCountFrequency (%)
MEDIUM30785
42.2%
LARGE8850
 
12.1%
MEDIUM SUV8090
 
11.1%
COMPACT7205
 
9.9%
VAN5854
 
8.0%
LARGE TRUCK3170
 
4.3%
SMALL SUV2276
 
3.1%
SPECIALTY1915
 
2.6%
CROSSOVER1759
 
2.4%
LARGE SUV1433
 
2.0%
Other values (2)1641
 
2.2%

Length

2022-06-09T17:16:55.101018image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
medium38875
43.8%
large13453
 
15.1%
suv11799
 
13.3%
compact7205
 
8.1%
van5854
 
6.6%
truck4034
 
4.5%
small3140
 
3.5%
specialty1915
 
2.2%
crossover1759
 
2.0%
sports777
 
0.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

TopThreeAmericanName
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing5
Missing (%)< 0.1%
Memory size570.3 KiB
GM
25314 
CHRYSLER
23399 
FORD
12315 
OTHER
11950 

Length

Max length8
Median length4
Mean length4.752528159
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOTHER
2nd rowCHRYSLER
3rd rowCHRYSLER
4th rowCHRYSLER
5th rowFORD

Common Values

ValueCountFrequency (%)
GM25314
34.7%
CHRYSLER23399
32.1%
FORD12315
16.9%
OTHER11950
16.4%
(Missing)5
 
< 0.1%

Length

2022-06-09T17:16:55.200369image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-09T17:16:55.258671image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
gm25314
34.7%
chrysler23399
32.1%
ford12315
16.9%
other11950
16.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

MMRAcquisitionAuctionAveragePrice
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct10342
Distinct (%)14.2%
Missing18
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean6128.909217
Minimum0
Maximum35722
Zeros828
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size570.3 KiB
2022-06-09T17:16:55.334483image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2583
Q14273
median6097
Q37765
95-th percentile10244
Maximum35722
Range35722
Interquartile range (IQR)3492

Descriptive statistics

Standard deviation2461.992768
Coefficient of variation (CV)0.4017016212
Kurtosis1.593727547
Mean6128.909217
Median Absolute Deviation (MAD)1743
Skewness0.4636405627
Sum447195861
Variance6061408.392
MonotonicityNot monotonic
2022-06-09T17:16:55.431828image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0828
 
1.1%
5480368
 
0.5%
5569233
 
0.3%
6311174
 
0.2%
6858137
 
0.2%
7644121
 
0.2%
4573110
 
0.2%
8196103
 
0.1%
689293
 
0.1%
704891
 
0.1%
Other values (10332)70707
96.9%
ValueCountFrequency (%)
0828
1.1%
8841
 
< 0.1%
8891
 
< 0.1%
9661
 
< 0.1%
10041
 
< 0.1%
10101
 
< 0.1%
10271
 
< 0.1%
10641
 
< 0.1%
10971
 
< 0.1%
11201
 
< 0.1%
ValueCountFrequency (%)
357221
< 0.1%
335431
< 0.1%
322501
< 0.1%
320631
< 0.1%
283541
< 0.1%
280771
< 0.1%
276801
< 0.1%
250331
< 0.1%
235811
< 0.1%
230311
< 0.1%

MMRAcquisitionAuctionCleanPrice
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct11379
Distinct (%)15.6%
Missing18
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean7373.636031
Minimum0
Maximum36859
Zeros697
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size570.3 KiB
2022-06-09T17:16:55.678113image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3500
Q15406
median7303
Q39021
95-th percentile12030.8
Maximum36859
Range36859
Interquartile range (IQR)3615

Descriptive statistics

Standard deviation2722.491986
Coefficient of variation (CV)0.369219741
Kurtosis1.651146564
Mean7373.636031
Median Absolute Deviation (MAD)1792
Skewness0.4665008675
Sum538017353
Variance7411962.611
MonotonicityNot monotonic
2022-06-09T17:16:55.790144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0697
 
1.0%
6461374
 
0.5%
6584225
 
0.3%
7450179
 
0.2%
8107133
 
0.2%
1131
 
0.2%
8892122
 
0.2%
9044109
 
0.1%
596796
 
0.1%
761489
 
0.1%
Other values (11369)70810
97.0%
ValueCountFrequency (%)
0697
1.0%
1131
 
0.2%
10761
 
< 0.1%
12401
 
< 0.1%
13971
 
< 0.1%
14371
 
< 0.1%
15351
 
< 0.1%
15871
 
< 0.1%
16101
 
< 0.1%
16341
 
< 0.1%
ValueCountFrequency (%)
368591
< 0.1%
367011
< 0.1%
352151
< 0.1%
351081
< 0.1%
304081
< 0.1%
301141
< 0.1%
294981
< 0.1%
280531
< 0.1%
267631
< 0.1%
256811
< 0.1%

MMRAcquisitionRetailAveragePrice
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct12725
Distinct (%)17.4%
Missing18
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean8497.034332
Minimum0
Maximum39080
Zeros828
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size570.3 KiB
2022-06-09T17:16:55.901331image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3617.2
Q16280
median8444
Q310651
95-th percentile13742
Maximum39080
Range39080
Interquartile range (IQR)4371

Descriptive statistics

Standard deviation3156.285284
Coefficient of variation (CV)0.3714572827
Kurtosis0.680599336
Mean8497.034332
Median Absolute Deviation (MAD)2188
Skewness0.2092142255
Sum619986110
Variance9962136.791
MonotonicityNot monotonic
2022-06-09T17:16:56.005875image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0828
 
1.1%
6418369
 
0.5%
6515230
 
0.3%
7316175
 
0.2%
7907136
 
0.2%
8756126
 
0.2%
9352103
 
0.1%
543996
 
0.1%
1111487
 
0.1%
794386
 
0.1%
Other values (12715)70729
96.9%
ValueCountFrequency (%)
0828
1.1%
14551
 
< 0.1%
15431
 
< 0.1%
15841
 
< 0.1%
15911
 
< 0.1%
16091
 
< 0.1%
16491
 
< 0.1%
17381
 
< 0.1%
17811
 
< 0.1%
18011
 
< 0.1%
ValueCountFrequency (%)
390801
< 0.1%
378851
< 0.1%
367261
< 0.1%
353301
< 0.1%
338721
< 0.1%
315991
< 0.1%
301961
< 0.1%
300481
< 0.1%
292691
< 0.1%
272951
< 0.1%

MMRAcquisitonRetailCleanPrice
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct13456
Distinct (%)18.4%
Missing18
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean9850.92824
Minimum0
Maximum41482
Zeros828
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size570.3 KiB
2022-06-09T17:16:56.130711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4646.2
Q17493
median9789
Q312088
95-th percentile15454.8
Maximum41482
Range41482
Interquartile range (IQR)4595

Descriptive statistics

Standard deviation3385.789541
Coefficient of variation (CV)0.3437025891
Kurtosis0.9248267411
Mean9850.92824
Median Absolute Deviation (MAD)2299
Skewness0.1762997563
Sum718772979
Variance11463570.82
MonotonicityNot monotonic
2022-06-09T17:16:56.228662image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0828
 
1.1%
7478373
 
0.5%
7611225
 
0.3%
8546171
 
0.2%
9256132
 
0.2%
10103113
 
0.2%
10268108
 
0.1%
694495
 
0.1%
1156283
 
0.1%
961382
 
0.1%
Other values (13446)70755
96.9%
ValueCountFrequency (%)
0828
1.1%
16621
 
< 0.1%
20091
 
< 0.1%
22391
 
< 0.1%
22651
 
< 0.1%
22671
 
< 0.1%
22741
 
< 0.1%
22841
 
< 0.1%
24001
 
< 0.1%
24011
 
< 0.1%
ValueCountFrequency (%)
414821
< 0.1%
403081
< 0.1%
401371
< 0.1%
385321
< 0.1%
360961
< 0.1%
339881
< 0.1%
337361
< 0.1%
327601
< 0.1%
323831
< 0.1%
299811
< 0.1%

MMRCurrentAuctionAveragePrice
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10315
Distinct (%)14.2%
Missing315
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean6132.081287
Minimum0
Maximum35722
Zeros504
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size570.3 KiB
2022-06-09T17:16:56.350973image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2617
Q14275
median6062
Q37736
95-th percentile10245.65
Maximum35722
Range35722
Interquartile range (IQR)3461

Descriptive statistics

Standard deviation2434.567723
Coefficient of variation (CV)0.3970214368
Kurtosis1.52993942
Mean6132.081287
Median Absolute Deviation (MAD)1726
Skewness0.52258321
Sum445606083
Variance5927119.998
MonotonicityNot monotonic
2022-06-09T17:16:56.458119image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0504
 
0.7%
5480280
 
0.4%
5569198
 
0.3%
6311142
 
0.2%
8186129
 
0.2%
7269126
 
0.2%
6858117
 
0.2%
7644101
 
0.1%
819695
 
0.1%
803384
 
0.1%
Other values (10305)70892
97.1%
(Missing)315
 
0.4%
ValueCountFrequency (%)
0504
0.7%
3691
 
< 0.1%
4301
 
< 0.1%
4331
 
< 0.1%
6652
 
< 0.1%
7271
 
< 0.1%
7331
 
< 0.1%
7401
 
< 0.1%
7991
 
< 0.1%
8061
 
< 0.1%
ValueCountFrequency (%)
357221
< 0.1%
333691
< 0.1%
322501
< 0.1%
311271
< 0.1%
280991
< 0.1%
277951
< 0.1%
275431
< 0.1%
234001
< 0.1%
230151
< 0.1%
219401
< 0.1%

MMRCurrentAuctionCleanPrice
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct11265
Distinct (%)15.5%
Missing315
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean7390.681827
Minimum0
Maximum36859
Zeros378
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size570.3 KiB
2022-06-09T17:16:56.576033image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3540
Q15414
median7313
Q39013
95-th percentile12032.3
Maximum36859
Range36859
Interquartile range (IQR)3599

Descriptive statistics

Standard deviation2686.248852
Coefficient of variation (CV)0.3634642804
Kurtosis1.565619526
Mean7390.681827
Median Absolute Deviation (MAD)1794
Skewness0.5355246484
Sum537066067
Variance7215932.892
MonotonicityNot monotonic
2022-06-09T17:16:56.678133image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0378
 
0.5%
6461284
 
0.4%
6584184
 
0.3%
7450140
 
0.2%
8107127
 
0.2%
1126
 
0.2%
8892103
 
0.1%
927999
 
0.1%
789899
 
0.1%
904495
 
0.1%
Other values (11255)71033
97.3%
(Missing)315
 
0.4%
ValueCountFrequency (%)
0378
0.5%
1126
 
0.2%
4941
 
< 0.1%
6511
 
< 0.1%
7931
 
< 0.1%
8971
 
< 0.1%
10051
 
< 0.1%
10722
 
< 0.1%
11321
 
< 0.1%
11681
 
< 0.1%
ValueCountFrequency (%)
368591
< 0.1%
364781
< 0.1%
352151
< 0.1%
347981
< 0.1%
301361
< 0.1%
298111
< 0.1%
290421
< 0.1%
264621
< 0.1%
261681
< 0.1%
258471
< 0.1%

MMRCurrentRetailAveragePrice
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12493
Distinct (%)17.2%
Missing315
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean8775.723331
Minimum0
Maximum39080
Zeros504
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size570.3 KiB
2022-06-09T17:16:56.807302image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3901.35
Q16536
median8729
Q310911
95-th percentile13853.65
Maximum39080
Range39080
Interquartile range (IQR)4375

Descriptive statistics

Standard deviation3090.702941
Coefficient of variation (CV)0.3521878282
Kurtosis0.6388495545
Mean8775.723331
Median Absolute Deviation (MAD)2188
Skewness0.2013561618
Sum637714263
Variance9552444.669
MonotonicityNot monotonic
2022-06-09T17:16:56.918565image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0504
 
0.7%
6418274
 
0.4%
6515184
 
0.3%
7316141
 
0.2%
7907118
 
0.2%
11674106
 
0.1%
8756103
 
0.1%
935294
 
0.1%
1083492
 
0.1%
543970
 
0.1%
Other values (12483)70982
97.3%
(Missing)315
 
0.4%
ValueCountFrequency (%)
0504
0.7%
8991
 
< 0.1%
9641
 
< 0.1%
9681
 
< 0.1%
12182
 
< 0.1%
12851
 
< 0.1%
12921
 
< 0.1%
12991
 
< 0.1%
13631
 
< 0.1%
13701
 
< 0.1%
ValueCountFrequency (%)
390801
< 0.1%
381511
< 0.1%
365391
< 0.1%
353301
< 0.1%
329281
< 0.1%
311281
< 0.1%
299211
< 0.1%
297521
< 0.1%
280501
< 0.1%
272691
< 0.1%

MMRCurrentRetailCleanPrice
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct13192
Distinct (%)18.2%
Missing315
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean10145.38531
Minimum0
Maximum41062
Zeros504
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size570.3 KiB
2022-06-09T17:16:57.037375image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4960
Q17784
median10103
Q312309
95-th percentile15615
Maximum41062
Range41062
Interquartile range (IQR)4525

Descriptive statistics

Standard deviation3310.254351
Coefficient of variation (CV)0.3262817772
Kurtosis0.8470080707
Mean10145.38531
Median Absolute Deviation (MAD)2260
Skewness0.1947799958
Sum737244860
Variance10957783.87
MonotonicityNot monotonic
2022-06-09T17:16:57.140231image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0504
 
0.7%
7478279
 
0.4%
7611186
 
0.3%
8546137
 
0.2%
9256114
 
0.2%
10103104
 
0.1%
1026895
 
0.1%
1286490
 
0.1%
1141389
 
0.1%
1238777
 
0.1%
Other values (13182)70993
97.3%
(Missing)315
 
0.4%
ValueCountFrequency (%)
0504
0.7%
10341
 
< 0.1%
12031
 
< 0.1%
13561
 
< 0.1%
14691
 
< 0.1%
15851
 
< 0.1%
16582
 
< 0.1%
17231
 
< 0.1%
18141
 
< 0.1%
18381
 
< 0.1%
ValueCountFrequency (%)
410621
< 0.1%
403081
< 0.1%
398961
< 0.1%
385321
< 0.1%
353661
< 0.1%
333371
< 0.1%
330141
< 0.1%
317441
< 0.1%
313171
< 0.1%
301941
< 0.1%

PRIMEUNIT
Boolean

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing69564
Missing (%)95.3%
Memory size142.7 KiB
False
 
3357
True
 
62
(Missing)
69564 
ValueCountFrequency (%)
False3357
 
4.6%
True62
 
0.1%
(Missing)69564
95.3%
2022-06-09T17:16:57.326072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

AUCGUART
Categorical

MISSING

Distinct2
Distinct (%)0.1%
Missing69564
Missing (%)95.3%
Memory size570.3 KiB
GREEN
3340 
RED
 
79

Length

Max length5
Median length5
Mean length4.953787657
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGREEN
2nd rowGREEN
3rd rowGREEN
4th rowGREEN
5th rowGREEN

Common Values

ValueCountFrequency (%)
GREEN3340
 
4.6%
RED79
 
0.1%
(Missing)69564
95.3%

Length

2022-06-09T17:16:57.392830image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-09T17:16:57.456725image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
green3340
97.7%
red79
 
2.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

BYRNO
Real number (ℝ≥0)

HIGH CORRELATION

Distinct74
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26345.84216
Minimum835
Maximum99761
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size570.3 KiB
2022-06-09T17:16:57.529900image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum835
5-th percentile1231
Q117212
median19662
Q322808
95-th percentile99761
Maximum99761
Range98926
Interquartile range (IQR)5596

Descriptive statistics

Standard deviation25717.35122
Coefficient of variation (CV)0.9761445873
Kurtosis3.474184178
Mean26345.84216
Median Absolute Deviation (MAD)2450
Skewness2.129224775
Sum1922798598
Variance661382153.7
MonotonicityNot monotonic
2022-06-09T17:16:57.642409image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
997613943
 
5.4%
188803588
 
4.9%
8352987
 
4.1%
34532927
 
4.0%
229162852
 
3.9%
210532816
 
3.9%
196192738
 
3.8%
997502653
 
3.6%
176752617
 
3.6%
209282586
 
3.5%
Other values (64)43276
59.3%
ValueCountFrequency (%)
8352987
4.1%
103131
 
< 0.1%
103510
 
< 0.1%
10418
 
< 0.1%
104562
 
0.1%
105173
 
0.1%
105512
 
< 0.1%
108137
 
0.1%
10827
 
< 0.1%
108550
 
0.1%
ValueCountFrequency (%)
997613943
5.4%
997602
 
< 0.1%
997502653
3.6%
997411
 
< 0.1%
99740343
 
0.5%
5324581
 
0.1%
52646347
 
0.5%
52644389
 
0.5%
52598587
 
0.8%
52492693
 
0.9%

VNZIP1
Real number (ℝ≥0)

HIGH CORRELATION

Distinct153
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58043.05995
Minimum2764
Maximum99224
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size570.3 KiB
2022-06-09T17:16:57.760657image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2764
5-th percentile22801
Q132124
median73108
Q380022
95-th percentile92807
Maximum99224
Range96460
Interquartile range (IQR)47898

Descriptive statistics

Standard deviation26151.64041
Coefficient of variation (CV)0.4505558535
Kurtosis-1.688690677
Mean58043.05995
Median Absolute Deviation (MAD)21436
Skewness-0.1035322325
Sum4236156644
Variance683908296.4
MonotonicityNot monotonic
2022-06-09T17:16:57.874065image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
328243699
 
5.1%
275423402
 
4.7%
752362431
 
3.3%
741352321
 
3.2%
800222118
 
2.9%
852262086
 
2.9%
850402012
 
2.8%
296971999
 
2.7%
956731970
 
2.7%
282731887
 
2.6%
Other values (143)49058
67.2%
ValueCountFrequency (%)
276415
 
< 0.1%
310697
 
0.1%
8505317
0.4%
125526
 
< 0.1%
160666
 
< 0.1%
161372
 
< 0.1%
1702833
 
< 0.1%
17406139
 
0.2%
17545136
 
0.2%
19440531
0.7%
ValueCountFrequency (%)
992245
 
< 0.1%
98064131
 
0.2%
9740211
 
< 0.1%
97217136
 
0.2%
9706064
 
0.1%
956731970
2.7%
94544752
 
1.0%
928071086
1.5%
92504385
 
0.5%
92337811
1.1%

VNST
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct37
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size570.3 KiB
TX
13596 
FL
10447 
CA
7095 
NC
7042 
AZ
6174 
Other values (32)
28629 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFL
2nd rowFL
3rd rowFL
4th rowFL
5th rowFL

Common Values

ValueCountFrequency (%)
TX13596
18.6%
FL10447
14.3%
CA7095
9.7%
NC7042
9.6%
AZ6174
8.5%
CO4998
 
6.8%
SC4280
 
5.9%
OK3594
 
4.9%
GA2450
 
3.4%
TN1764
 
2.4%
Other values (27)11543
15.8%

Length

2022-06-09T17:16:57.966838image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tx13596
18.6%
fl10447
14.3%
ca7095
9.7%
nc7042
9.6%
az6174
8.5%
co4998
 
6.8%
sc4280
 
5.9%
ok3594
 
4.9%
ga2450
 
3.4%
tn1764
 
2.4%
Other values (27)11543
15.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

VehBCost
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2010
Distinct (%)2.8%
Missing68
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean6729.249949
Minimum1
Maximum45469
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size570.3 KiB
2022-06-09T17:16:58.062230image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4030
Q15430
median6700
Q37900
95-th percentile9775
Maximum45469
Range45468
Interquartile range (IQR)2470

Descriptive statistics

Standard deviation1764.962643
Coefficient of variation (CV)0.2622822241
Kurtosis8.084871664
Mean6729.249949
Median Absolute Deviation (MAD)1230
Skewness0.7019715512
Sum490663260
Variance3115093.131
MonotonicityNot monotonic
2022-06-09T17:16:58.167094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7500777
 
1.1%
6500588
 
0.8%
7200586
 
0.8%
6000582
 
0.8%
4200553
 
0.8%
7000550
 
0.8%
8000541
 
0.7%
7800516
 
0.7%
7100460
 
0.6%
7400456
 
0.6%
Other values (2000)67306
92.2%
ValueCountFrequency (%)
11
< 0.1%
2251
< 0.1%
14001
< 0.1%
16201
< 0.1%
17201
< 0.1%
19151
< 0.1%
19201
< 0.1%
19602
< 0.1%
20001
< 0.1%
20102
< 0.1%
ValueCountFrequency (%)
454691
< 0.1%
387851
< 0.1%
364851
< 0.1%
359001
< 0.1%
323001
< 0.1%
297951
< 0.1%
285601
< 0.1%
281801
< 0.1%
203801
< 0.1%
201001
< 0.1%

IsOnlineSale
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size570.3 KiB
0
71138 
1
 
1845

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
071138
97.5%
11845
 
2.5%

Length

2022-06-09T17:16:58.271152image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-09T17:16:58.323707image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
071138
97.5%
11845
 
2.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

WarrantyCost
Real number (ℝ≥0)

HIGH CORRELATION

Distinct281
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1276.580985
Minimum462
Maximum7498
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size570.3 KiB
2022-06-09T17:16:58.389939image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum462
5-th percentile569
Q1837
median1155
Q31623
95-th percentile2198
Maximum7498
Range7036
Interquartile range (IQR)786

Descriptive statistics

Standard deviation598.8467882
Coefficient of variation (CV)0.4691020745
Kurtosis9.964807664
Mean1276.580985
Median Absolute Deviation (MAD)351
Skewness2.070831169
Sum93168710
Variance358617.4757
MonotonicityNot monotonic
2022-06-09T17:16:58.495388image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9202870
 
3.9%
19742407
 
3.3%
21522082
 
2.9%
13892078
 
2.8%
12152006
 
2.7%
11551878
 
2.6%
8031690
 
2.3%
7281663
 
2.3%
15031599
 
2.2%
10861579
 
2.2%
Other values (271)53131
72.8%
ValueCountFrequency (%)
462412
 
0.6%
482738
1.0%
5051005
1.4%
52217
 
< 0.1%
5331174
1.6%
55374
 
0.1%
5691393
1.9%
582165
 
0.2%
588182
 
0.2%
594932
1.3%
ValueCountFrequency (%)
74981
 
< 0.1%
71981
 
< 0.1%
68192
 
< 0.1%
65191
 
< 0.1%
64922
 
< 0.1%
62082
 
< 0.1%
61922
 
< 0.1%
591336
< 0.1%
59082
 
< 0.1%
561335
< 0.1%

pMonth
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.608018306
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size570.3 KiB
2022-06-09T17:16:58.592566image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.415753593
Coefficient of variation (CV)0.5169104314
Kurtosis-1.250251544
Mean6.608018306
Median Absolute Deviation (MAD)3
Skewness-0.04871561071
Sum482273
Variance11.66737261
MonotonicityNot monotonic
2022-06-09T17:16:58.660675image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
107178
9.8%
26839
9.4%
116675
9.1%
96511
8.9%
36257
8.6%
86139
8.4%
45935
8.1%
75876
8.1%
55760
7.9%
65675
7.8%
Other values (2)10138
13.9%
ValueCountFrequency (%)
14770
6.5%
26839
9.4%
36257
8.6%
45935
8.1%
55760
7.9%
65675
7.8%
75876
8.1%
86139
8.4%
96511
8.9%
107178
9.8%
ValueCountFrequency (%)
125368
7.4%
116675
9.1%
107178
9.8%
96511
8.9%
86139
8.4%
75876
8.1%
65675
7.8%
55760
7.9%
45935
8.1%
36257
8.6%

season
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size570.3 KiB
fall
20364 
spring
17952 
summer
17690 
winter
16977 

Length

Max length6
Median length6
Mean length5.441952235
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowwinter
2nd rowwinter
3rd rowwinter
4th rowwinter
5th rowwinter

Common Values

ValueCountFrequency (%)
fall20364
27.9%
spring17952
24.6%
summer17690
24.2%
winter16977
23.3%

Length

2022-06-09T17:16:58.753469image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-09T17:16:58.805147image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
fall20364
27.9%
spring17952
24.6%
summer17690
24.2%
winter16977
23.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-06-09T17:16:48.986106image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:20.787660image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:22.699266image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:24.511610image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:26.335785image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:28.332471image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:30.307280image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:32.272817image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:34.138501image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:36.108890image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:37.933744image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:39.901851image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:41.659046image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:43.470322image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:45.362291image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:47.065592image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:49.085696image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:20.906991image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:22.788432image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:24.616374image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:26.459067image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:28.439029image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:30.424213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:32.374029image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:34.243742image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:36.218688image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:38.035379image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:40.003233image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:41.875627image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:43.571276image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:45.460795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:47.170988image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:49.187220image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:21.016227image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:22.886235image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:24.720407image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:26.560458image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:28.562733image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:30.541735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:32.481306image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:34.360103image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:36.318795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:38.158955image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:40.105430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:41.982895image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:43.685904image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:45.564232image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:47.275743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:49.292747image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:21.118818image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:23.006245image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:24.840470image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:26.682496image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:28.675225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:30.656964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:32.600147image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:34.471616image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:36.434320image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:38.274258image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:40.219198image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:42.085366image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:43.789714image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:45.671087image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:47.389639image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:49.396131image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:21.308546image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:23.105509image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:24.963359image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:26.801806image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:28.791431image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:30.784776image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:32.702423image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:34.593242image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:36.557241image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:38.382911image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:40.338954image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:42.200995image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:43.892903image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:45.787887image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:47.625005image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:49.517153image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:21.423671image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:23.225820image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:25.081833image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:26.906223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:28.916414image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:30.893898image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:32.822263image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:34.719836image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:36.674030image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:38.505298image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:40.453874image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:42.300480image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:44.024298image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:45.894562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:47.728638image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:49.633614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:21.538983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:23.324754image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:25.187956image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:27.028983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:29.035427image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:31.026821image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:32.922820image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:34.838247image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:36.784853image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:38.635854image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:40.566940image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:42.420479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:44.135343image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:46.010309image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:47.849309image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:49.741675image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:21.649678image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:23.435834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:25.302003image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:27.260115image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:29.154820image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:31.127957image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:33.140346image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:34.943702image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:36.897292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:38.751061image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:40.682036image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:42.524257image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:44.285849image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:46.102465image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:47.958108image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:49.854486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:21.761738image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:23.543464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:25.405500image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:27.380395image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:29.268228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:31.245695image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:33.259844image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:35.075536image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:37.016913image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:38.984610image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:40.785956image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:42.636864image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:44.403189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:46.220437image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:48.066444image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:49.974318image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:21.869670image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:23.656315image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:25.527724image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-06-09T17:16:29.389663image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:31.367612image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:33.368570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:35.189588image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:37.131554image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:39.104251image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:40.902031image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:42.745633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:44.521829image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:46.337970image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:48.190900image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-06-09T17:16:21.990673image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-06-09T17:16:29.516582image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:31.505025image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:33.485520image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:35.314965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:37.252325image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:39.218730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:41.019289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-06-09T17:16:44.741176image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:46.445607image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:48.315474image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:50.209439image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:22.080762image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:23.866038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:25.808192image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:27.762458image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:29.628391image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-06-09T17:16:35.418521image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:37.375027image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:39.335383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:41.106963image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:42.968960image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:44.852629image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:46.558378image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:48.412345image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:50.302396image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:22.238087image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:23.972885image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:25.919872image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:27.869286image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:29.736581image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:31.767390image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:33.708481image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:35.534387image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:37.486284image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:39.450065image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:41.222960image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:43.072046image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:44.952906image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:46.662345image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:48.531070image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:50.530067image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:22.337729image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:24.075315image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:26.024187image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:27.990616image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:29.859481image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:31.903246image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:33.810827image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:35.642000image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:37.591991image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:39.556843image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:41.319500image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:43.175584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:45.056478image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:46.750804image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:48.647944image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:50.627879image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:22.442502image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:24.302840image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:26.125355image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:28.095694image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:29.964964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:32.026700image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:33.912855image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:35.747324image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:37.700414image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:39.665396image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:41.420803image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:43.273530image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:45.153999image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:46.849658image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:48.765769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:50.729113image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:22.582279image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:24.400263image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:26.230077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:28.214738image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:30.202834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:32.153008image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:34.022906image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:35.872896image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:37.816738image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:39.784340image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:41.556626image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:43.373434image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:45.255081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:46.969053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-09T17:16:48.874447image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-06-09T17:16:59.004069image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-06-09T17:16:59.222107image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-06-09T17:16:59.434100image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-06-09T17:16:59.638871image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-06-09T17:16:59.811983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-06-09T17:16:50.960762image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-06-09T17:16:51.822733image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-06-09T17:16:52.486368image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-06-09T17:16:52.847176image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

IsBadBuyPurchDateAuctionVehYearVehicleAgeMakeModelTrimSubModelColorTransmissionWheelTypeIDWheelTypeVehOdoNationalitySizeTopThreeAmericanNameMMRAcquisitionAuctionAveragePriceMMRAcquisitionAuctionCleanPriceMMRAcquisitionRetailAveragePriceMMRAcquisitonRetailCleanPriceMMRCurrentAuctionAveragePriceMMRCurrentAuctionCleanPriceMMRCurrentRetailAveragePriceMMRCurrentRetailCleanPricePRIMEUNITAUCGUARTBYRNOVNZIP1VNSTVehBCostIsOnlineSaleWarrantyCostpMonthseason
002009-12-07ADESA20063MAZDAMAZDA3i4D SEDAN IREDAUTO1.0Alloy89046OTHER ASIANMEDIUMOTHER8155.09829.011636.013600.07451.08552.011597.012409.0NaNNaN2197333619FL7100.00111312winter
102009-12-07ADESA20045DODGE1500 RAM PICKUP 2WDSTQUAD CAB 4.7L SLTWHITEAUTO1.0Alloy93593AMERICANLARGE TRUCKCHRYSLER6854.08383.010897.012572.07456.09222.011374.012791.0NaNNaN1963833619FL7600.00105312winter
202009-12-07ADESA20054DODGESTRATUS V6SXT4D SEDAN SXT FFVMAROONAUTO2.0Covers73807AMERICANMEDIUMCHRYSLER3202.04760.06943.08457.04035.05557.07146.08702.0NaNNaN1963833619FL4900.00138912winter
302009-12-07ADESA20045DODGENEONSXT4D SEDANSILVERAUTO1.0Alloy65617AMERICANCOMPACTCHRYSLER1893.02675.04658.05690.01844.02646.04375.05518.0NaNNaN1963833619FL4100.0063012winter
402009-12-07ADESA20054FORDFOCUSZX32D COUPE ZX3SILVERMANUAL2.0Covers69367AMERICANCOMPACTFORD3913.05054.07723.08707.03247.04384.06739.07911.0NaNNaN1963833619FL4000.00102012winter
502009-12-07ADESA20045MITSUBISHIGALANT 4CES4D SEDAN ESWHITEAUTO2.0Covers81054OTHER ASIANMEDIUMOTHER3901.04908.06706.08577.04709.05827.08149.09451.0NaNNaN1963833619FL5600.0059412winter
602009-12-07ADESA20045KIASPECTRAEX4D SEDAN EXBLACKAUTO2.0Covers65328OTHER ASIANMEDIUMOTHER2966.04038.06240.08496.02980.04115.06230.08603.0NaNNaN1963833619FL4200.0053312winter
702009-12-07ADESA20054FORDTAURUSSE4D SEDAN SEWHITEAUTO2.0Covers65805AMERICANMEDIUMFORD3313.04342.06667.07707.03713.04578.06942.08242.0NaNNaN1963833619FL4500.0082512winter
802009-12-07ADESA20072KIASPECTRAEX4D SEDAN EXBLACKAUTO2.0Covers49921OTHER ASIANMEDIUMOTHER6196.07274.09687.010624.06417.07371.09637.010778.0NaNNaN2197333619FL5600.0048212winter
902009-12-07ADESA20072FORDFIVE HUNDREDSEL4D SEDAN SELREDAUTO1.0Alloy84872AMERICANLARGEFORD7845.09752.011734.013656.09167.010988.012580.014845.0NaNNaN2197333619FL7700.00163312winter

Last rows

IsBadBuyPurchDateAuctionVehYearVehicleAgeMakeModelTrimSubModelColorTransmissionWheelTypeIDWheelTypeVehOdoNationalitySizeTopThreeAmericanNameMMRAcquisitionAuctionAveragePriceMMRAcquisitionAuctionCleanPriceMMRAcquisitionRetailAveragePriceMMRAcquisitonRetailCleanPriceMMRCurrentAuctionAveragePriceMMRCurrentAuctionCleanPriceMMRCurrentRetailAveragePriceMMRCurrentRetailCleanPricePRIMEUNITAUCGUARTBYRNOVNZIP1VNSTVehBCostIsOnlineSaleWarrantyCostpMonthseason
7297302009-11-24ADESA20063CHRYSLER300Bas4D SEDANGREENAUTO2.0Covers68127AMERICANSPECIALTYCHRYSLER8929.010605.010143.011953.08721.010295.012398.014231.0NaNNaN1811130212GA7600.00121511fall
7297402009-12-02ADESA20027DODGE1500 RAM PICKUP 2WDSTQUAD CAB 4.7L SLTGOLDAUTO1.0Alloy93744AMERICANLARGE TRUCKCHRYSLER5485.06823.06424.07869.06201.07686.09169.010173.0NaNNaN1811130212GA7500.00135312winter
7297502009-12-02ADESA20072HYUNDAISONATA V6Lim4D SEDANSILVERAUTO1.0Alloy74407OTHER ASIANMEDIUMOTHER7712.09614.08829.010883.07453.09004.011776.013041.0NaNNaN1811130212GA8000.0080312winter
7297602009-12-02ADESA20045FORDEXPLORER 2WD V6XLS4D SUV 4.0L FFV XLSSILVERAUTO1.0Alloy82563AMERICANMEDIUM SUVFORD4668.05714.05541.06671.06148.07521.09659.010944.0NaNNaN1888130212GA7000.00124312winter
7297702009-12-02ADESA20063KIASORENTO 2WDEX4D SPORT UTILITY EXGOLDAUTO1.0Alloy65399OTHER ASIANMEDIUM SUVOTHER7843.09171.08970.010405.07652.09310.012148.014204.0NaNNaN1811130212GA7900.00150812winter
7297812009-12-02ADESA20018MERCURYSABLEGS4D SEDAN GSBLACKAUTO1.0Alloy45234AMERICANMEDIUMFORD1996.02993.02656.03732.02190.03055.04836.05937.0NaNNaN1811130212GA4200.0099312winter
7297902009-12-02ADESA20072CHEVROLETMALIBU 4CLS4D SEDAN LSSILVERAUTONaNNaN71759AMERICANMEDIUMGM6418.07325.07431.08411.06785.08132.010151.011652.0NaNNaN1888130212GA6200.00103812winter
7298002009-12-02ADESA20054JEEPGRAND CHEROKEE 2WD VLar4D WAGON LAREDOSILVERAUTO1.0Alloy88500AMERICANMEDIUM SUVCHRYSLER8545.09959.09729.011256.08375.09802.011831.014402.0NaNNaN1811130212GA8200.00189312winter
7298102009-12-02ADESA20063CHEVROLETIMPALALS4D SEDAN LSWHITEAUTO1.0Alloy79554AMERICANLARGEGM6420.07604.07434.08712.06590.07684.010099.011228.0NaNNaN1888130212GA7000.00197412winter
7298202009-12-02ADESA20063MAZDAMAZDA6s4D SEDAN SSILVERAUTO1.0Alloy66855OTHER ASIANMEDIUMOTHER7535.08771.08638.09973.07730.09102.011954.013246.0NaNNaN1811130212GA8000.00131312winter